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import random
import argparse
from typing import Optional
import cv2
import imageio
import numpy as np
import numpy.typing as npt
import torch
from decord import VideoReader, cpu
from torch.nn import functional as F
from pytorchvideo.transforms import ShortSideScale
from torchvision.transforms import Lambda, Compose
from torchvision.transforms._transforms_video import RandomCropVideo
import sys
sys.path.append(".")
from opensora.models.ae import VQVAEModel
def array_to_video(image_array: npt.NDArray, fps: float = 30.0, output_file: str = 'output_video.mp4') -> None:
height, width, channels = image_array[0].shape
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # type: ignore
video_writer = cv2.VideoWriter(output_file, fourcc, float(fps), (width, height))
for image in image_array:
image_rgb = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
video_writer.write(image_rgb)
video_writer.release()
def custom_to_video(x: torch.Tensor, fps: float = 2.0, output_file: str = 'output_video.mp4') -> None:
x = x.detach().cpu()
x = torch.clamp(x, -0.5, 0.5)
x = (x + 0.5)
x = x.permute(1, 2, 3, 0).numpy() # (C, T, H, W) -> (T, H, W, C)
x = (255*x).astype(np.uint8)
# array_to_video(x, fps=fps, output_file=output_file)
imageio.mimwrite(output_file, x, fps=fps, quality=9)
return
def read_video(video_path: str, num_frames: int, sample_rate: int) -> torch.Tensor:
decord_vr = VideoReader(video_path, ctx=cpu(0))
total_frames = len(decord_vr)
sample_frames_len = sample_rate * num_frames
if total_frames > sample_frames_len:
s = random.randint(0, total_frames - sample_frames_len - 1)
e = s + sample_frames_len
num_frames = num_frames
else:
s = 0
e = total_frames
num_frames = int(total_frames / sample_frames_len * num_frames)
print(f'sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}', video_path,
total_frames)
frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
video_data = decord_vr.get_batch(frame_id_list).asnumpy()
video_data = torch.from_numpy(video_data)
video_data = video_data.permute(3, 0, 1, 2) # (T, H, W, C) -> (C, T, H, W)
return video_data
def preprocess(video_data: torch.Tensor, short_size: int = 128, crop_size: Optional[int] = None) -> torch.Tensor:
transform = Compose(
[
# UniformTemporalSubsample(num_frames),
Lambda(lambda x: ((x / 255.0) - 0.5)),
# NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
ShortSideScale(size=short_size),
RandomCropVideo(size=crop_size) if crop_size is not None else Lambda(lambda x: x),
# RandomHorizontalFlipVideo(p=0.5),
]
)
video_outputs = transform(video_data)
video_outputs = torch.unsqueeze(video_outputs, 0)
return video_outputs
def main(args: argparse.Namespace):
video_path = args.video_path
num_frames = args.num_frames
resolution = args.resolution
crop_size = args.crop_size
sample_fps = args.sample_fps
sample_rate = args.sample_rate
device = torch.device('cuda')
if args.ckpt in ['bair_stride4x2x2', 'ucf101_stride4x4x4', 'kinetics_stride4x4x4', 'kinetics_stride2x4x4']:
vqvae = VQVAEModel.download_and_load_model(args.ckpt)
else:
vqvae = VQVAEModel.load_from_checkpoint(args.ckpt)
vqvae.eval()
vqvae = vqvae.to(device)
with torch.no_grad():
x_vae = preprocess(read_video(video_path, num_frames, sample_rate), resolution, crop_size)
x_vae = x_vae.to(device)
encodings, embeddings = vqvae.encode(x_vae, include_embeddings=True)
video_recon = vqvae.decode(encodings)
# custom_to_video(x_vae[0], fps=sample_fps/sample_rate, output_file='origin_input.mp4')
custom_to_video(video_recon[0], fps=sample_fps/sample_rate, output_file=args.rec_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--video-path', type=str, default='')
parser.add_argument('--rec-path', type=str, default='')
parser.add_argument('--ckpt', type=str, default='ucf101_stride4x4x4')
parser.add_argument('--sample-fps', type=int, default=30)
parser.add_argument('--resolution', type=int, default=336)
parser.add_argument('--crop-size', type=int, default=None)
parser.add_argument('--num-frames', type=int, default=100)
parser.add_argument('--sample-rate', type=int, default=1)
args = parser.parse_args()
main(args)
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